Analyzing multilingual knowledge innovation in patents

In the process of analyzing knowledge innovation, it is necessary to identify the existing boundaries of knowledge so as to determine whether knowledge is new - outside these boundaries. For a patent to be granted, all aspects of the patent request must be studied to determine the patent innovation. Knowledge innovation for patent requests depends on analyzing current state of the art in multiple languages. Currently the process is usually limited to the languages and search terms the patent seeker knows. The paper describes a model for representing the patent request by a set of concepts related to a multilingual knowledge ontology. The search for patent knowledge is based on Fuzzy Logic Decision Support and allows a multilingual search. The model was analyzed using a twofold approach: a total of 104,296 patents from the United States Patent and Trademark Office were used to analyze the patent extraction process, and patents from the Korean, US, and Chinese patent offices were used in the analysis of the multilingual decision process. The results display high recall and precision and suggest that increasing the number of languages used only has minor effects on the model results.

[1]  Pedro M. Domingos,et al.  Representing and reasoning about mappings between domain models , 2002, AAAI/IAAI.

[2]  Aviv Segev,et al.  Patent Search Decision Support Service , 2010, 2010 Seventh International Conference on Information Technology: New Generations.

[3]  Dario Lucarella,et al.  FIRST: Fuzzy Information Retrieval SysTem , 1991, J. Inf. Sci..

[4]  John Shawe-Taylor,et al.  Advanced learning algorithms for cross-language patent retrieval and classification , 2007, Inf. Process. Manag..

[5]  Michael Kifer,et al.  Logical foundations of object-oriented and frame-based languages , 1995, JACM.

[6]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.

[7]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[8]  Lotfi A. Zadeh,et al.  Commonsense Knowledge Representation Based on Fuzzy Logic , 1983, Computer.

[9]  Dae-Won Kim,et al.  Fuzzy Information Retrieval Indexed by Concept Identification , 2005, TSD.

[10]  Sergey Melnik,et al.  Generic Model Management , 2004, Lecture Notes in Computer Science.

[11]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.

[12]  Mark A. Musen,et al.  PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment , 2000, AAAI/IAAI.

[13]  Carol Peters,et al.  CLEF 2009 Ad Hoc Track Overview: TEL and Persian Tasks , 2009, CLEF.

[14]  Mario Bunge,et al.  The furniture of the world , 1977 .

[15]  S. Miyamoto.  Information retrieval based on fuzzy associations , 1990 .

[16]  Moshe Zviran,et al.  Internet as a knowledge base for medical diagnostic assistance , 2007, Expert Syst. Appl..

[17]  Avigdor Gal,et al.  Automatic Ontology Matching Using Application Semantics , 2005, AI Mag..

[18]  B. C. Vickery,et al.  Faceted classification schemes , 1966 .

[19]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[20]  Steffen Staab,et al.  Ontology Learning for the Semantic Web , 2002, IEEE Intell. Syst..

[21]  Deborah L. McGuinness,et al.  An Environment for Merging and Testing Large Ontologies , 2000, KR.

[22]  Noriko Kando,et al.  The patent retrieval task in the fourth NTCIR workshop , 2004, SIGIR '04.

[23]  Sergey Melnik,et al.  Generic Model Management: Concepts And Algorithms (Lecture Notes in Computer Science) , 2004 .

[24]  Aviv Segev,et al.  Identification of trends from patents using self-organizing maps , 2012, Expert Syst. Appl..

[25]  Avigdor Gal,et al.  Enhancing portability with multilingual ontology-based knowledge management , 2008, Decis. Support Syst..

[26]  Avigdor Gal,et al.  Putting Things in Context : A Topological Approach to Mapping Contexts and Ontologies , 2005 .

[27]  Andrew Basden,et al.  New research directions for data and knowledge engineering: A philosophy of language approach , 2008, Data Knowl. Eng..

[28]  Pedro M. Domingos,et al.  Learning to map between ontologies on the semantic web , 2002, WWW '02.

[29]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[30]  M. Bunge.  Treatise on basic philosophy , 1974 .

[31]  Han Tong Loh,et al.  Grouping of TRIZ Inventive Principles to facilitate automatic patent classification , 2008, Expert Syst. Appl..

[32]  I. V. Ramakrishnan,et al.  OntoMiner: Bootstrapping and Populating Ontologies from Domain-Specific Web Sites , 2003, IEEE Intell. Syst..

[33]  Valerie Cros,et al.  Fuzzy information retrieval , 1994, Journal of Intelligent Information Systems.

[34]  Charles W. Evans.  Encyclopedia of Library and Information Science. Vol. 8. Allen Kent , Harold Lancour , William Z. Nasri , 1974 .

[35]  Noriko Kando,et al.  Evaluating patent retrieval in the third NTCIR workshop , 2006, Inf. Process. Manag..

[36]  Vincenzo Loia,et al.  Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis , 2012, Inf. Process. Manag..

[37]  Yiannis Kompatsiaris,et al.  Towards content-oriented patent document processing , 2008 .

[38]  Robert Meersman,et al.  Data modelling versus ontology engineering , 2002, SGMD.

[39]  Francesco M. Donini,et al.  Reasoning in description logics , 1997 .

[40]  Xue Li,et al.  Classifying text streams by keywords using classifier ensemble , 2011, Data Knowl. Eng..

[41]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[42]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[43]  Ujjwal Maulik,et al.  Soft Computing and its Applications , 2011 .

[44]  Chih-Ping Wei,et al.  A Latent Semantic Indexing-based approach to multilingual document clustering , 2008, Decis. Support Syst..

[45]  Ian Horrocks,et al.  OWL Web Ontology Language Reference-W3C Recommen-dation , 2004 .

[46]  Alexander Borgida,et al.  Loading data into description reasoners , 1993, SIGMOD Conference.

[47]  Jérôme Euzenat,et al.  A modest proposal for data interlinking evaluation , 2012, OM.

[48]  Xiangji Huang,et al.  TREC-CHEM: large scale chemical information retrieval evaluation at TREC , 2009, SIGF.

[49]  John Tait,et al.  CLEF-IP 2009: Retrieval Experiments in the Intellectual Property Domain , 2009, CLEF.

[50]  Raúl E. Valdés-Pérez,et al.  Concise, intelligible, and approximate profiling of multiple classes , 2000, Int. J. Hum. Comput. Stud..

[51]  Piek Vossen,et al.  EuroWordNet: general document , 2002 .

[52]  Jens Lehmann,et al.  RAVEN - active learning of link specifications , 2011, OM.

[53]  Stephen E. Robertson,et al.  Understanding inverse document frequency: on theoretical arguments for IDF , 2004, J. Documentation.